code: def test_regularization(self):
      penalty = self.model.get_regularization_penalty()
      assert penalty is None
  
      data_loader = SimpleDataLoader(self.instances, batch_size=32)
      trainer = GradientDescentTrainer(
          self.model, None, data_loader
      )  # optimizer,
  
      # You get a RuntimeError if you call `model.forward` twice on the same inputs.
      # The data and config are such that the whole dataset is one batch.
      training_batch = next(iter(data_loader))
      validation_batch = next(iter(data_loader))
  
      training_loss = trainer.batch_outputs(
          training_batch, for_training=True
      )["loss"].item()
      validation_loss = trainer.batch_outputs(
          validation_batch, for_training=False
      )["loss"].item()
  
      # Training loss should have the regularization penalty, but validation loss should not.
      numpy.testing.assert_almost_equal(training_loss, validation_loss)
  
imports: from flaky import flaky
  import numpy
  import pytest
  import torch
  from allennlp.common.testing import ModelTestCase
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.params import Params
  from allennlp.data.dataset_readers import DatasetReader
  from allennlp.data.data_loaders import DataLoader, SimpleDataLoader
  from allennlp.models import Model
  from allennlp.training import GradientDescentTrainer, Trainer
label_desc: Use image node_id 7 for calling the SimpleTaggerTest obj's underlying member method code with example usage: obj.test_regularization() without return types